Cloud-Native Transformations: Microservices, Kubernetes, and Security Frameworks in Practice

Introduction: Grassroots football plays a crucial role in developing young talent and fostering a passion for the game through community initiatives, youth academies, and local clubs. Successful soccer nations like Germany and Spain have leveraged structured youth development systems to produce elite players, contributing to their international triumphs. In contrast, the United States faces challenges in translating its widespread grassroots participation into global success, largely due to the restrictive pay-to-play model, which limits access for talented players from lower socioeconomic backgrounds. This study examines effective grassroots development strategies from Europe, South America, and Africa to provide recommendations for enhancing the U.S. system. By adopting a more inclusive and structured approach to talent identification and development, the U.S. can improve its national team performance on the global stage. Materials and Methods: This study adopted a systematic review technique following PRISMA criteria to guarantee a controlled and rigorous assessment of grassroots soccer development. A comprehensive search across various databases, including Web of Science, Scopus, IEEE Xplore, ACM Digital Library, and Google Scholar, revealed 1,942 relevant publications, which were refined through a rigorous selection process, eventually selecting 158 high-quality sources. Inclusion criteria centered on peer-reviewed research published between 2010 and 2023, while exclusion criteria removed opinion-based studies and those lacking a clear emphasis on grassroots soccer. Data extraction using a standardized pro forma, and analysis was accomplished through story and theme synthesis. To guarantee validity and reliability, the Mixed Methods Appraisal Tool (MMAT) and Critical Appraisal Skills Program (CASP) checklist were applied, along with inter-rater agreement evaluations. Bias was minimized using multi-database searches, independent reviewer evaluations, and consensus talks, producing a robust and trustworthy systematic review. Results: The primary challenges with American soccer originate from structural and financial restrictions that hinder talent development and accessibility. The pay-to-play approach restricts chances for lower-income players, diminishing diversity and the total skill pool. Unlike successful worldwide models, the U.S. lacks a cohesive academy structure, resulting to fragmented player routes and variable development standards. Coaching education and license are not as stringent or supported as in leading soccer nations, hurting training quality at all levels. There is also a restricted culture of unstructured play, which inhibits creativity and technical ability in young players. Infrastructure discrepancies also increase the disparity, with impoverished populations having less quality training facilities. Additionally, there is insufficient synergy between grassroots academies, professional clubs, and the national team structure, making it difficult to migrate players successfully. Addressing these difficulties demands institutional improvements to make soccer more inclusive, organized, and development-focused. Discussion: The conversation stressed that effective grassroots soccer development relies on established academies, good coaching education, financial accessibility, and seamless connection between clubs and national teams. Countries like Germany, Spain, Brazil, Argentina, Nigeria, and Senegal have proved that investing in young development through well-organized systems, informal play settings, and talent reinvestment boosts national team success. In contrast, the U.S. has issues owing to its pay-to-play economy, fragmented growth routes, and lack of infrastructure in marginalized neighborhoods. To strengthen its system, the U.S. must reduce financial obstacles, expand coaching education, invest in grassroots facilities, encourage unstructured play chances, and build a uniform academy structure. Strengthening ties with premier foreign clubs and reinvesting talent-generated cash can further promote long-term player development. By implementing these improvements, U.S. Soccer can develop a more inclusive and efficient structure that optimizes talent potential and promotes national team performance. Conclusion: Global grassroots soccer models give vital insights for strengthening the U.S. system. Europe stresses organized academies and club-led development, South America thrives on casual play and strong club structures, while Africa benefits from private scouting and European relationships. The U.S. has problems like as the pay-to-play market, variable coaching standards, and a fragmented growth route. Adopting effective worldwide tactics may build a more inclusive and competitive soccer framework, increasing talent development and national team performance.

Literature review is a fundamental part of a research process, and systematic protocols for this activity have been used for a long time, mainly in the field of health. Specifically in the Computer Science Education area, the use of systematic literature review has grown. One of the steps in a systematic literature review (SLR) is the selection of academic databases in which to search for articles. There are several databases with academic documents that may be relevant to SLR, for example: Google Scholar, which indexes different types of documents, such as articles, dissertations, theses, and others; Scopus and Web of Science are large databases that index articles from different conferences and journals. ACM Digital Library and IEEE Xplore are also important sources of information in the field of Computer Education. These tools have different characteristics, some charge a fee, others have only information about the title and authors and do not have access to the full article, others have advanced features, with many filters. In this context, this article presents the following research questions: RQ1) What metadata can be extracted automatically from the databases?; RQ2) What kind of visualization tools are available?; RQ3) Do the documents returned by the databases cover the research topic?; RQ4) Do the databases have papers from the main CSE venues?; and RQ5) How many databases are required to perform a literature review in CSE? To answer these questions we used five academic databases: Google Scholar, Scopus, Web of Science, ACM Digital Library, and IEEE xplore. Regarding the results, Scopus and Web of Science have the best visualization of the documents and a robust query engine, however those academic databases are not free. ACM Digital library, IEEE Xplore, Scopus and Web of Science allow the automatic download of the papers’ metadata (author, title, abstract, affiliation and others). Specifically in the field of Computer Science Education, the ACM Digital Library and the IEEE Xplore have important papers from conferences (SIGCSE and FIE) and journals (ACM Transaction on Education and IEEE Transaction on Education). In this full paper, the results will be presented to help researchers to choose the most appropriate academic databases based on their requirements and available options.

An OLDL (Online Literature Digital Library) is a library in which collections, i.e., publications from one or more domains of study, are stored in digital formats (as opposed to print, microform, or other media) and accessible by users through the Internet. Examples of wellknown OLDLs are IEEE Xplore (IEEE Xplore, 2008), ACM Portal (ACM Digital Library, 2008), CiteSeer (CiteSeer, 2008), Google Scholar (Google Scholar, 2008), and PubMed (PubMed, 2008). Digital libraries are rapidly growing in popularity. For instance, ScienceDirect (ScienceDirect, 2008), the world’s leading scientific, technical and medical information resource celebrated its billionth article download in November’06 since launched in 1999. Besides usage, digital libraries are also rapidly growing in terms of size and diversity of topics. For instance, (i) in Computer Science, ACM Digital Library (ACM Digital Library, 2008) has close to one million full-text publications collected over 50 years, to search and download; (ii) in Electrical Engineering and Computer Science, IEEE Xplore (IEEE Xplore, 2008), another OLDL, provides users with on-line access to more than 1,700 selected conferences proceedings. These high growth rates introduced several challenges facing the information access capability of OLDLs. Next we list few challenges that probably guides future research related to LDLs. Challenge 1: Large Sizes and Topic Diversity of Search Output Results. Search outputs of OLDLs tend to suffer from the “topic diffusion” problem, where commonly, keyword-based searches produce a large number of publications over a large number of topics, where not all topics are of interest to the user. One way to solve this problem is to assign scores to search results ( i.e., publications). Assigning scores to publications helps OLDLs to present the most important relevant publications to the user first, Citation-based publication score measures (e.g., citation count) are commonly used for ranking publications. At the present time, OLDLs lack effective and accurate publication ranking. Challenge 2: Lack of Effective Scoring Functions for Publications. At the present time, OLDLs lack effective and accurate publication rankings (Ratprasartporn et al., 2007). Providing accurate publication scores can help users in reducing the time spent in searching OLDLs, and thus enhances the scalability of OLDL usage as users can quickly identify important relevant publications to their topic of interest.

ObjectiveThis review paper aims to evaluate existing solutions in healthcareauthentication and provides an insight into the technologies incorporated inInternet of Healthcare Things (IoHT) and multi-factor authentication (MFA)applications for next-generation authentication practices. Our review hastwo objectives: (a) Review MFA based on the challenges, impact and solutionsdiscussed in the literature; and (b) define the security requirements of theIoHT as an approach to adapting MFA solutions in a healthcare context.MethodsTo review the existing literature, we indexed articles from the IEEE Xplore,ACM Digital Library, ScienceDirect, and SpringerLink databases. The searchwas refined to combinations of ‘authentication’, ‘multi-factorauthentication’, ‘Internet of Things authentication’, and ‘medicalauthentication’ to ensure that the retrieved journal articles and conferencepapers were relevant to healthcare and Internet of Things-orientedauthentication research.ResultsThe concepts of MFA can be applied to healthcare where security can often beoverlooked. The security requirements identified result in strongermethodologies of authentication such as hardware solutions in combinationwith biometric data to enhance MFA approaches. We identify the keyvulnerabilities of weaker approaches to security such as password useagainst various cyber threats. Cyber threats and MFA solutions arecategorised in this paper to facilitate readers’ understanding of them inhealthcare domains.ConclusionsWe contribute to an understanding of up-to-date MFA approaches and how theycan be improved for use in the IoHT. This is achieved by discussing thechallenges, benefits, and limitations of current methodologies andrecommendations to improve access to eHealth resources through additionallayers of security.

Microservice architecture (MSA) has emerged as a significant software architecture in both industry and academia, garnering attention as a key research area. MSA offers numerous benefits, such as facilitating independent development, testing, and system deployment. However, its distributed characteristics introduce various challenges. Testing microservices presents diverse challenges derived from complex dependencies, diverse execution environments, and different development teams, which lessen the effectiveness of conventional testing methods. Through a systematic literature review, we aim to present an overview of various testing strategies, testing tools, innovative testing solutions, and associated challenges. Our research includes academic resources from IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Wiley Online Library. From this research, we identified 76 primary studies, which we analyzed through thematic synthesis. Our analysis yielded 16 distinct testing strategies for microservices-based systems, accompanied by 19 distinct categories of proposed solutions for microservices testing. Furthermore, we compiled a total of 95 relevant tools for testing microservices. We also documented 22 distinct categories of challenges specific to testing microservices-based systems, including but not limited to automated testing, performance evaluation, and the creation of MSA-based testing environments. This study aims to serve as a resource for software engineering professionals and researchers interested in microservices testing.

Artificial intelligence (AI) has changed the way we protect ourselves from cyber threats by giving us better tools for finding threats, lowering risks, and responding in real time. As cyber threats get more complicated and widespread, it is important to strategically integrate and manage AI-powered technologies in cybersecurity frameworks. This systematic review brings together the most recent studies on how to strategically manage AI-driven cybersecurity systems. It points out the best ways to use them across industries, as well as their pros and cons. 87 peer-reviewed articles from 2015 to 2024 were examined that were found in databases such Scopus, IEEE Xplore, SpringerLink, and ScienceDirect. We used the PRISMA standards to do this. The review finds five main themes: (1) AI algorithms for finding and classifying threats; (2) AI governance and risk management; (3) problems with integrating AI into security frameworks in organizations; (4) ethical and legal issues; and (5) strategic deployment and scalability. The results show that AI makes threat intelligence and adaptive reaction much better, but companies have problems with explainability, data privacy, and algorithmic bias. Human-AI collaboration, continuous learning loops, and regulatory compliance frameworks are all examples of strategic management techniques that make AI integration more effective. The evaluation also stresses that to make good AI strategy, you need to have knowledge in a variety of fields, such as data science, cyber law, and behavioral analytics. In conclusion, strategic management is very important for getting the most out of AI in cybersecurity. To create cybersecurity ecosystems that are strong, flexible, and ethical, we need to take a proactive strategy that includes aligning policies, training the workforce, and managing the lifecycle.

Background: Sustainable digital transformation requires more than technological adoption; it depends on strategic knowledge management (KM). Fourth Industrial Revolution (4IR) technologies such as artificial intelligence (AI), the Internet of Things (IoT), blockchain and big data have reshaped organisational knowledge creation, sharing and application. However, the mediating role of KM between digital capability and sustainability outcomes remains insufficiently integrated in the literature. Objectives: This review examines how KM supports sustainable digital transformation, how 4IR technologies influence KM processes, and which barriers and enablers shape knowledge-sharing ecosystems. It also develops a conceptual framework grounded in the knowledge-based view and dynamic capabilities theories to explain how KM converts digital capability into sustainable organisational performance. Method: A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Peer-reviewed publications from 2015 to 2024 were retrieved from IEEE Xplore, Scopus, SpringerLink, Web of Science and the ACM Digital Library. Selected studies were screened and thematically analysed. Results: Five themes emerged: KM and Industry 4.0, Digital Transformation and KM, Strategic KM Frameworks, Challenges in KM Implementation, and General KM Insights. The findings of this study indicate that KM enhances innovation, organisational agility and sustainability when aligned with 4IR technologies. Cultural, infrastructural and knowledge-security barriers remain significant constraints. Conclusion: Knowledge management is central to sustainable digital transformation. Organisations must adopt adaptive and technology-aligned KM strategies to achieve long-term value. Contribution: The study synthesises dominant research themes and proposes an integrative framework positioning KM as the mediating mechanism through which 4IR technologies generate sustainable organisational outcomes.

The Morocco 2030 World Cup, a tri-continental mega-event co-hosted with Spain and Portugal, will feature complex digital infrastructures spanning multiple nations, creating an expansive attack surface for cyber threats. As mega-events become increasingly digitized, the convergence of physical and digital security domains renders these high-profile gatherings particularly vulnerable to ransomware, data breaches, and disinformation campaigns, with consequences extending beyond operational disruption to national security and host nation reputation. This systematic review synthesizes existing research on threat detection and international cybersecurity standards for large-scale events, with particular attention to the Morocco 2030 World Cup as an illustrative case. The review aims to evaluate cyber threat detection methods, benchmark international standards, identify lessons from prior mega-events, compare risk and resilience frameworks, and assess the role of emerging technologies. A systematic literature search was conducted across Scopus, Web of Science, IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, and Google Scholar. The search yielded 39 papers, supplemented by 86 additional records through backward and forward citation chaining. Following full-text screening and relevance assessment, 50 studies were included in the final synthesis. Data extraction captured bibliographic information, study characteristics, thematic focus, and key findings, which were analyzed using thematic synthesis. The review identifies a pervasive "implementation gap" between theoretical consensus and practical realization. While AI-enhanced threat detection demonstrates significant potential for improving speed and accuracy (15 studies), empirical validation in live mega-event environments remains limited. International standards such as NIST and ISO/IEC 27001 provide essential governance frameworks (20 studies), yet adoption is inconsistent and hindered by voluntary compliance. Lessons from previous World Cups and Olympics (20 studies) emphasize multi-agency coordination and contextual adaptation but reveal challenges in sustaining security legacies. Risk and resilience frameworks support adaptive security (18 studies) but lack event-specific tailoring. Emerging technologies, notably AI-driven zero-trust architectures (15 studies), offer transformative promise but face deployment challenges and a maturity gap. Securing mega-events like Morocco 2030 requires integrated strategies combining advanced detection, standardized frameworks, stakeholder collaboration, and contextual intelligence. Future research must prioritize empirical validation of AI-driven architectures in live event settings and the development of binding international cybersecurity standards tailored to the unique temporal and operational realities of mega-events, ensuring security legacies that are both effective and socially sustainable. Keywords: Cybersecurity, Mega-Events, Threat Detection, World Cup, Morocco 2030, AI, Risk Assessment.

The rapid adoption of artificial intelligence (AI) in healthcare and other high-risk environments has introduced sophisticated cyber threats that challenge traditional security models. Zero Trust Architecture (ZTA), with its principle of "never trust, always verify," has emerged as a promising framework to counter these evolving risks. This systematic review examines the current state of ZTA implementations in mitigating AI-driven cyber threats, focusing on healthcare systems, and identifies gaps between theoretical principles and real-world applications. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we conducted a comprehensive search across five databases (IEEE Xplore, PubMed, Scopus, Web of Science, and ACM Digital Library), identifying 299 records. After removing duplicates and screening for relevance, 15 studies met the inclusion criteria. These studies were analyzed for themes related to ZTA components, AI threat mitigation, implementation challenges, and ethical considerations. The Mixed Methods Appraisal Tool (MMAT) was used to assess methodological quality and risk of bias. The review revealed that while ZTA principles are well-suited to address AI-driven threats, particularly through explainable AI (XAI) and continuous monitoring, significant gaps persist in standardization, empirical validation, and stakeholder trust. Key findings include (1) a lack of metrics to evaluate ZTA efficacy against adversarial AI; (2) ethical and regulatory hurdles, such as algorithmic bias and data privacy concerns; and (3) operational barriers like interoperability issues and clinician resistance. Only four of the 15 studies provided real-world evidence of ZTA implementations, highlighting a critical research-practice divide. ZTA represents a transformative approach to cybersecurity in AI-augmented environments, but its potential remains underutilized due to theoretical dominance and implementation challenges. Future efforts must prioritize interdisciplinary collaboration, standardized frameworks, and pilot studies to bridge these gaps. Without actionable advancements, ZTA risks being outpaced by the very AI threats it seeks to mitigate. This review underscores the urgent need for adaptive, evidence-based ZTA models tailored to high-risk sectors, such as healthcare.

In modern software engineering, it has gained much attention as an effective paradigm to traditional monolithic architectures. Microservices provide an application development approach in a module-based way, where larger systems are divided into a number of small, independently deployable, and loosely coupled services. It performs pre-defined functions, with loose coupling among these services via APIs, which interact with other services; thus, modifications or failures in one will not bring down the whole application. This architecture thus gives faster development cycles, continuous delivery, and scaling of individual components depending upon demand, making it quite useful in dynamic and large-scale cloud environments. The wide adoption of cloud computing platforms such as AWS, Microsoft Azure, and Google Cloud has accelerated further the growth of microservices-based systems. Cloud infrastructure provides the necessary tools that are vital for the seamless deployment and management of microservices at scale, such as automated provisioning, scaling, monitoring, and orchestration. This enables the use of cloud-based containerization technologies such as Docker and Kubernetes to easily isolate services and efficiently orchestrate and manage distributed applications. These cloud environments also provide robust service discovery mechanisms, load balancing, and fault-tolerance features necessary for maintaining system reliability and availability in production environments. However, migration toward a microservices-based architecture introduces a different set of challenges that requires careful consideration of various aspects of software engineering. At the design level, a key aspect that would affect this architecture’s success pertains to clearly establishing the proper service boundaries, data consistency, and assurance that services align well with the business capabilities. Additional concerns in the flexibility provided by microservices come in the complexity of their communications, management of their data, and keeping this across a distributed system in a consistent manner. Ensuring seamless communication between services, in particular when dealing with eventual consistency models and distributed transactions, is one of the important design challenges in microservices systems. The deployment phase involves the adoption of best practices for continuous integration and continuous delivery, the utilization of automated build pipelines, and the deployment of services in containers to ensure smooth and efficient releases. The elasticity of the cloud allows organizations to scale services up or down, depending on demand. Additionally, orchestrators like Kubernetes can automate deployment and the management of containers across a set of nodes. However, all these advantages require a very effective monitoring strategy that will guarantee performance, identify bottlenecks, and handle failures. Latency, throughput, and error rates are some metrics that need to be monitored relentlessly in order to keep a system healthy, and it’s important to set automated alerts for proactive issue resolution. Managing microservices at a production level is actually far beyond just deployment; there should be continuous monitoring of the performance of the system, interaction management of services, fault processing, and security ensuring of the application. Since microservices architectures can have hundreds or thousands of services, the adoption of centralized logging, distributed tracing, and performance management tools becomes critical for holistic views of system health. In addition, the organization should put in place strong fault tolerance mechanisms like circuit breakers and retries to ensure that the system stays up even when some of its constituent services fail. Security in microservices-based architectures is another critical consideration, as the distributed nature of microservices increases the attack surface, necessitating effective authentication, authorization, and encryption strategies to protect sensitive data. This research explores microservices architecture from a software engineering perspective, with a focus on its design, deployment, and management within cloud computing environments. This paper presents the evolution of practices and strategies for adopting microservices in cloud-native environments through an extensive review of existing literature, industry case studies, and expert interviews. The paper aims to highlight best practices for designing microservices that are scalable, resilient, and maintainable. Furthermore, it provides a detailed examination of deployment strategies, particularly those involving CI/CD pipelines, containerization, and orchestration tools. It finally goes into the operational challenges of managing microservices at scale, including how to monitor, tolerate faults, and keep microservices secure in cloud environments. Apart from the theoretical view, this paper provides practical recommendations that will enable software engineers and organizations to manage or adopt microservices architecture without getting lost in the way. The work contributes to the growing literature on cloud-based microservices by addressing a number of challenges related to service decomposition, interservice communication, data consistency, deployment pipelines, and system management. The findings presented in this study aim to provide both academics and industry professionals with an in-depth understanding of how to effectively implement and manage microservices architectures, thereby making software solutions more efficient, reliable, and scalable in cloud computing environments.

[1]. Artificial Intelligence (AI) is revolutionizing cybersecurity by improving threat identification, mitigation, and protection strategies. As cyber threats become more complex and sophisticated, AI-driven solutions play a key role in strengthening the security architecture and ensuring proactive protection, AIdriven solutions offer proactive defense strategies, real-time network monitoring, and automated security protocols. This research employs the The Gray Correlation Analysis (GRA) method is used to evaluate the performance of AI in five important domains: communication protocols (C1), node security (C2), network monitoring (C3), cryptography (C4), and security policy (C5). study highlights how AI optimizes security frameworks, mitigates cyber risks, and strengthens overall defense mechanisms. The results indicate that AI significantly improves cybersecurity resilience by addressing vulnerabilities across multiple layers of security. Research Significance: Cyber threats are evolving, requiring intelligent and adaptive security measures AI enhances conventional cybersecurity approaches by automating threat detection and mitigation. Assessing the impact of AI on the security architecture helps improve security mechanisms and response effectiveness. impact on key security components provides insights into its effectiveness. Methodology: Grey Relational Analysis (GRA) Grey Relational Analysis (GRA) is used to assess the relationship between multiple security factors in AI-driven cybersecurity. GRA helps in ranking and determining the most effective AI applications in cybersecurity by analyzing Communication Protocols (C1), Node Security (C2), Network Monitoring (C3), Cryptography (C4), and Security Policy (C5). Alternative Approaches: Communication Protocol (C1): AI-based secure communication frameworks and anomaly detection in data exchange. Node Security (C2): AI-driven authentication, endpoint protection, and intrusion detection at the node level. Network Monitoring (C3): AI-powered network traffic analysis, anomaly detection, and automated threat response. Cryptography (C4): AI-assisted encryption, quantumresistant algorithms, and secure key management. Security Policy (C5): AI-enhanced policy enforcement, adaptive security frameworks, and compliance monitoring. Evaluation Parameters: Threat Intelligence AI analyzes vast datasets to predict, dentify, analyze, and neutralize cyber threats by recognizing attack patterns and vulnerabilities before exploitation. Intrusion Detection and Prevention AI enhances Intrusion Detection Systems (IDS) and Through Intrusion Prevention Methods (IPM) identifying malicious activities and blocking attacks proactively. Malware Detection and Analysis AI-powered cybersecurity solutions detect, classify, and neutralize malware using machine learning algorithms and behavioral analysis. User and Entity Behavior Analytics (UEBA) AI monitors Monitor user behavior to identify abuse, prevent unauthorized access, and detect insider threats enhancing cybersecurity posture. Automated Incident Response AI accelerates cybersecurity responses by automating threat mitigation, reducing human intervention, and minimizing damage from cyberattacks. Results: The study findings indicate that AI significantly improves cybersecurity across all parameters. AI-driven network monitoring (C3) and cryptography (C4) exhibit the highest impact in mitigating threats. Node security (C2) and communication protocols (C1) demonstrate enhanced efficiency in securing endpoints and data exchange. Security policies (C5) benefit from AI-driven automation, ensuring compliance and real-time adaptation to threats. The GRA analysis highlights AI plays a key role in improving cybersecurity resilience and reducing the likelihood of cyber threats attacks.

Microservices architecture has emerged as a transformative paradigm in modern software development, enabling organizations to build resilient and scalable distributed systems. This comprehensive exploration delves into the fundamental principles, implementation strategies, and real-world applications of microservices architecture. The architectural framework facilitates independent service deployment, enhanced fault isolation, and streamlined maintenance processes while promoting technology stack flexibility. Through the implementation of sophisticated monitoring patterns, testing strategies, and data consistency mechanisms, organizations can effectively address common challenges in distributed systems. The integration of enterprise architecture principles with microservices has demonstrated significant improvements in resource utilization, system reliability, and operational efficiency. The adoption of API gateway patterns, security frameworks, and containerization technologies further enhances system performance and scalability. Real-world applications, particularly in e-commerce platforms, showcase the practical benefits of microservices architecture in managing complex business operations while maintaining high availability and performance standards.

Objetivo: Este estudo tem como objetivo analisar o uso de Sistemas de Organização do Conhecimento (SOC) como meio de enriquecimento do Prontuário Eletrônico do Paciente (PEP) para o domínio da oncologia pediátrica. Métodos: Foi aplicado um método de revisão integrativa da literatura. Foram realizadas três revisões de literatura, com busca de artigos de 2016 até Julho/2023 em PubMed, Scopus, IEEE Xplore e ACM Digital Library escritos em Inglês ou Português. Resultados: Foram analisados 52 artigos. Os resultados apontam os padrões adotados para a especificação de PEP e descrevem os SOC mais frequentemente usados com PEP na oncologia e também no domínio da oncologia pediátrica. Conclusão: Embora existam esforços para adotar padrões internacionais para PEP, vários projetos não fazem uso desses padrões. Os sistemas de PEPs para oncologia, em geral, fazem uso mais amplo de SOCs, enquanto na oncologia pediátrica o foco está nos relacionados à genética. Há necessidade de mais pesquisas para integrar PEP com padrões internacionais.

Background: Unmanned Aerial Vehicles (UAVs) relay nets and Flying Ad Hoc Networks (FANETs) have become effective facilitators of applications like disaster management, surveillance, smart transportation and military logistics. Compared to the conventional mobile ad hoc networks, FANETs are subject to extreme mobility, three-dimensional movement, intermittency, and use of limited onboard resources, which present a great challenge to routing reliability, network performance as well as security. Objective: This paper would be a systematic review of the literature on the UAV relay networks and FANETs with emphasis on routing protocols, challenges related to mobility, performance analysis, and security threats and solutions. Methods: The systematic review was performed based on PRISMA 2020. A wide literature search on IEEE Xplore, Scopus, Web of Science, ACM Digital Library, and Google Scholar revealed the presence of appropriate literature within the last five years (2012-2025). After screening and quality evaluation, 137 peer reviewed articles were found by carefully sifting through a plethora of literature. This was achieved through narrative analysis of data and it was backed by comparative tables and graphical illustrations. Results: The findings show that position-based (geographic) routing protocols always experience better packet delivery ratios (6595) and lower delay than topology-based routing protocols especially in high-mobility and dense FANET environment. As UAV speed is increased, breakages of links and poor routing stability increases, indicating the constraints of protocols which do not have mobility prediction mechanisms. Security analysis indicates that FANETs are susceptible to black hole attack which involves wormhole, GPS spoofing attack, Sybil, and denial of service attack. New countermeasures that take advantage of lightweight cryptography, trust-based routing, blockchain technology, and AI-based intrusion detection systems hold potential, but at the cost of energy consumption and algorithm costs still remain. Conclusion: The conclusion of the review is that the performance of FANET is greatly affected by mobility dynamics, the choice of routing strategy, and security integration. The use of position-based and intelligent routing methods is more scalable and robust, and the security issue is still a research question. Future studies are needed on holistic, adaptive, and secure routing schemes, experimental studies, and cross-layer optimization to aid reliable large scale FANET implementations.

The growing adoption of microservice architectures (MSAs) has led to major research and development efforts to address their challenges and improve their performance, reliability, and robustness. Important aspects of MSA that are not sufficiently covered in the open literature include efficient cloud resource allocation and optimal power management. Other aspects of MSA remain widely scattered in the literature, including cost analysis, service level agreements (SLAs), and demand-driven scaling. In this article, we examine recent cloud frameworks for containerized microservices with a focus on efficient resource utilization using auto-scaling. We classify these frameworks on the basis of their resource allocation models and underlying hardware resources. We highlight current MSA trends and identify workload-driven resource sharing within microservice meshes and SLA streamlining as two key areas for future microservice research.